Dataversity

MARCH 24, 2021

Enterprises migrating on-prem data environments to the cloud in pursuit of more robust, flexible, and integrated analytics and AI/ML capabilities are fueling a surge in cloud data lake implementations. The post How to Ensure Your New Cloud Data Lake Is Secure appeared first on DATAVERSITY.

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Data Lakes Cloud Data ML ML 59

Flipboard

MARCH 7, 2023

This post presents a solution that uses a workflow and AWS AI and machine learning (ML) services to provide actionable insights based on those transcripts. We use multiple AWS AI/ML services, such as Contact Lens for Amazon Connect and Amazon SageMaker , and utilize a combined architecture.

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ML ML AWS AI 123

IBM Data Science in Practice

JANUARY 9, 2024

Many businesses are in different stages of their MAS AI/ML modernization journey. In this blog, we delve into 4 different “on-ramps” we created in a MAS Accelerator to offer a straightforward path to harnessing the power of AI in MAS, wherever you may be on your MAS AI/ML modernization journey.

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. SageMaker Studio is the first fully integrated development environment (IDE) for ML. The next step is to build ML models using features selected from one or multiple feature groups.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. In addition to several exciting announcements during keynotes, most of the sessions in our track will feature generative AI in one form or another, so we can truly call our track “Generative AI and ML.”

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Flipboard

JUNE 26, 2023

Companies are faced with the daunting task of ingesting all this data, cleansing it, and using it to provide outstanding customer experience. Typically, companies ingest data from multiple sources into their data lake to derive valuable insights from the data. Run the AWS Glue ML transform job.

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ODSC - Open Data Science

JUNE 1, 2023

Real-Time ML with Spark and SBERT, AI Coding Assistants, Data Lake Vendors, and ODSC East Highlights Getting Up to Speed on Real-Time Machine Learning with Spark and SBERT Learn more about real-time machine learning by using this approach that uses Apache Spark and SBERT. Well, these libraries will give you a solid start.

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Data Lakes ML ML Citizen Data Scientist 52

AWS Machine Learning Blog

AUGUST 15, 2024

Starting today, you can interactively prepare large datasets, create end-to-end data flows, and invoke automated machine learning (AutoML) experiments on petabytes of data—a substantial leap from the previous 5 GB limit. Organizations often struggle to extract meaningful insights and value from their ever-growing volume of data.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

Principal is conducting enterprise-scale near-real-time analytics to deliver a seamless and hyper-personalized omnichannel customer experience on their mission to make financial security accessible for all. They are processing data across channels, including recorded contact center interactions, emails, chat and other digital channels.

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Flipboard

DECEMBER 18, 2023

Amazon Redshift powers data-driven decisions for tens of thousands of customers every day with a fully managed, AI-powered cloud data warehouse, delivering the best price-performance for your analytics workloads.

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Data Science Dojo

SEPTEMBER 10, 2024

LLM companies are businesses that specialize in developing and deploying Large Language Models (LLMs) and advanced machine learning (ML) models. WhyLabs WhyLabs is renowned for its versatile and robust machine learning (ML) observability platform. What are LLM Companies?

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Machine Learning Machine Learning Natural Language Processing ML 369

ODSC - Open Data Science

JUNE 7, 2023

To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering. What is a data lake? First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data.

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Data Lakes Azure Data Warehouse Hadoop 52

AWS Machine Learning Blog

AUGUST 21, 2024

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Enterprises can use no-code ML solutions to streamline their operations and optimize their decision-making without extensive administrative overhead.

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Machine Learning Machine Learning Data Governance ML 120

AWS Machine Learning Blog

APRIL 3, 2025

At the heart of this transformation is the OMRON Data & Analytics Platform (ODAP), an innovative initiative designed to revolutionize how the company harnesses its data assets. The robust security features provided by Amazon S3, including encryption and durability, were used to provide data protection.

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AWS Data Governance Data Silos SQL 80

AWS Machine Learning Blog

FEBRUARY 21, 2025

Thats why we use advanced technology and data analytics to streamline every step of the homeownership experience, from application to closing. Data exploration and model development were conducted using well-known machine learning (ML) tools such as Jupyter or Apache Zeppelin notebooks.

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Data Science AWS Hadoop Data Scientist 74

AWS Machine Learning Blog

AUGUST 8, 2024

As one of the largest AWS customers, Twilio engages with data, artificial intelligence (AI), and machine learning (ML) services to run their daily workloads. Data is the foundational layer for all generative AI and ML applications. The following diagram illustrates the solution architecture.

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SQL Data Lakes Data Analyst AWS 125

Data Science Connect

JULY 28, 2023

As the Internet of Things (IoT) continues to revolutionize industries and shape the future, data scientists play a crucial role in unlocking its full potential. A recent article on Analytics Insight explores the critical aspect of data engineering for IoT applications.

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AWS Machine Learning Blog

SEPTEMBER 27, 2024

Solution overview Amazon SageMaker is a fully managed service that helps developers and data scientists build, train, and deploy machine learning (ML) models. Data preparation SageMaker Ground Truth employs a human workforce made up of Northpower volunteers to annotate a set of 10,000 images.

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AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. This solution helps market analysts design and perform data-driven bidding strategies optimized for power asset profitability. Data comes from disparate sources in a number of formats.

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AWS Machine Learning Blog

NOVEMBER 27, 2023

SageMaker endpoints can be registered to the Salesforce Data Cloud to activate predictions in Salesforce. SageMaker Canvas provides a no-code experience to access data from Salesforce Data Cloud and build, test, and deploy models using just a few clicks. On the Create menu, choose Tabular to create a tabular dataset.

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ML ML AWS SQL 96

Data Science 101

NOVEMBER 11, 2019

Azure Synapse Analytics This is the future of data warehousing. It combines data warehousing and data lakes into a simple query interface for a simple and fast analytics service. Azure Arc You can now run Azure services anywhere (on-prem, on the edge, any cloud) you can run Kubernetes. Amazon Web Services.

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Cloud Data Data Science Azure Clustering 95

Flipboard

NOVEMBER 17, 2023

Amazon SageMaker enables enterprises to build, train, and deploy machine learning (ML) models. Amazon SageMaker JumpStart provides pre-trained models and data to help you get started with ML. MongoDB vector data store MongoDB Atlas Vector Search is a new feature that allows you to store and search vector data in MongoDB.

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K-nearest Neighbors AWS Clustering Database 170

AWS Machine Learning Blog

JUNE 5, 2023

Data is the foundation for machine learning (ML) algorithms. One of the most common formats for storing large amounts of data is Apache Parquet due to its compact and highly efficient format. In this post, we describe how to query Parquet files with Athena using AWS Lake Formation and use the output Canvas to train a model.

Machine Learning 95

Machine Learning Machine Learning AWS Data Lakes 95

AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. An enterprise might have the following roles involved in the ML lifecycles. This ML platform provides several key benefits.

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ML ML Data Scientist AWS 63

How to Learn Machine Learning

MAY 2, 2025

Azure Machine Learning is Microsoft’s enterprise-grade service that provides a comprehensive environment for data scientists and ML engineers to build, train, deploy, and manage machine learning models at scale. You can explore its capabilities through the official Azure ML Studio documentation. Awesome, right?

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Azure Machine Learning Machine Learning Data Science 52

IBM Journey to AI blog

APRIL 24, 2023

By running reports on historical data, a data warehouse can clarify what systems and processes are working and what methods need improvement. Data warehouse is the base architecture for artificial intelligence and machine learning (AI/ML) solutions as well. Modern data warehousing technology can handle all data forms.

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Data Warehouse Data Lakes Database Big Data 112

AWS Machine Learning Blog

DECEMBER 4, 2023

We capitalized on the powerful tools provided by AWS to tackle this challenge and effectively navigate the complex field of machine learning (ML) and predictive analytics. This capability of predictive analytics, particularly the accurate forecast of product categories, has proven invaluable.

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AWS Machine Learning Blog

AUGUST 4, 2023

If you are a returning user to SageMaker Studio, in order to ensure Salesforce Data Cloud is enabled, upgrade to the latest Jupyter and SageMaker Data Wrangler kernels. This completes the setup to enable data access from Salesforce Data Cloud to SageMaker Studio to build AI and machine learning (ML) models.

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ML ML AWS AI 97

AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

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AI AI ML ML 126

IBM Journey to AI blog

OCTOBER 4, 2023

Another IDC study showed that while 2/3 of respondents reported using AI-driven data analytics, most reported that less than half of the data under management is available for this type of analytics. from 2022 to 2026.

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Data Lakes Data Warehouse AI AI 95

Smart Data Collective

FEBRUARY 20, 2022

Whether it’s data management, analytics, or scalability, AWS can be the top-notch solution for any SaaS company. Data storage databases. Your SaaS company can store and protect any amount of data using Amazon Simple Storage Service (S3), which is ideal for data lakes, cloud-native applications, and mobile apps.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines.

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AWS Natural Language Processing Machine Learning Machine Learning 121

ODSC - Open Data Science

MAY 8, 2023

Article on Azure ML by Bethany Jepchumba and Josh Ndemenge of Microsoft In this article, I will cover how you can train a model using Notebooks in Azure Machine Learning Studio. Using Azure ML, you can train your model in three ways: Automated ML: This is where you upload your data and have the workspace automatically train on your behalf.

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Azure ML ML Data Modeling 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Machine Learning Machine Learning ML ML 134

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. This tool automatically detects problems in an ML dataset.

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Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

AWS Machine Learning Blog

FEBRUARY 13, 2024

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, share, and manage features for machine learning (ML) models. Features are inputs to ML models used during training and inference. SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts.

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AWS ML ML Machine Learning 128

The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

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